Zero-shot black-box detection of neural trojans

Abstract

Example systems, methods, and apparatus are disclosed herein for zero-shot black-box detection of neural Trojans.

The invention is claimed as follows: 
     
         1 . A system for zero-shot black-box detection of neural Trojans comprising:
 a server;   a processor;   memory storing instructions, which when executed by the processor, cause the processor to apply a zero-shot black-box detection of neural Trojans algorithm.   
     
     
         2 . The system of  claim 1 , wherein the zero-shot black-box detection of neural Trojans algorithm is: 
       
         
           
                 
                 
               
                     
                 
                   1: 
                   X ← random Trigger( ) 
                 
                   2:  
                   for k = 1, . . . , s do 
                 
                     
                 
                   3: 
                    
       T   ←     ϵ   ·     (       1       ?     +     ?         -     1       ?     +     ?           )           
         ?     indicates text missing or illegible when filed       
 
                 
                     
                 
                   4: 
                    X new  ← randomNeighbor(X) 
                 
                   5: 
                    C old  ← cASR(X) 
                 
                   6: 
                    C new  ← cASR(X new ) 
                 
                   7: 
                    Δ C  ← C new  − C old   
                 
                   8: 
                    if Δ C  > 0 then 
                 
                   9: 
                     X ← X new   
                 
                     
                 
                   10: 
                    
         else   ⁢         if   ⁢           e       Δ   C     T         ≥       random   (     0   ,   1     )     ⁢        then         
 
                 
                     
                 
                   11: 
                     X ← X new   
                 
                   12:  
                    end if 
                 
                   13:  
                   end for 
                 
                     
                 
             
                
               
               
                
                
                
                
                
                
                
                
                
                
                
                
                
                
                
                
                
                
               
            
           
         
       
     
     
         3 . A method of using zero-shot black-box detection of neural Trojans comprising:
 receiving a patch;   performing simulated annealing on the patch using a zero-shot black-box detection of neural Trojans algorithm;   detecting the presence of a Trojan.   
     
     
         4 . The method of  claim 3 , wherein the patch includes at least one of a random pattern, a size, a shape, or a location. 
     
     
         5 . The method of  claim 4 , wherein the zero-shot black-box detection of neural Trojans algorithm is: 
       
         
           
                 
                 
               
                     
                 
                   1: 
                   X ← random Trigger( ) 
                 
                   2:  
                   for k = 1, . . . , s do 
                 
                     
                 
                   3: 
                    
       T   ←     ϵ   ·     (       1       ?     +     ?         -     1       ?     +     ?           )           
         ?     indicates text missing or illegible when filed       
 
                 
                     
                 
                   4: 
                    X new  ← randomNeighbor(X) 
                 
                   5: 
                    C old  ← cASR(X) 
                 
                   6: 
                    C new  ← cASR(X new ) 
                 
                   7: 
                    Δ C  ← C new  − C old   
                 
                   8: 
                    if Δ C  > 0 then 
                 
                   9: 
                     X ← X new   
                 
                     
                 
                   10: 
                    
         else   ⁢         if   ⁢           e       Δ   C     T         ≥       random   (     0   ,   1     )     ⁢        then         
 
                 
                     
                 
                   11: 
                     X ← X new   
                 
                   12:  
                    end if 
                 
                   13:  
                   end for